Overview

Dataset statistics

Number of variables20
Number of observations97463
Missing cells158882
Missing cells (%)8.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.9 MiB
Average record size in memory160.0 B

Variable types

Numeric6
Categorical5
Text6
Unsupported1
DateTime2

Alerts

Fiscal_Year has constant value ""Constant
CheckVoidDt has constant value ""Constant
ObjectId is highly overall correlated with Budget_Type and 1 other fieldsHigh correlation
InvoiceID is highly overall correlated with CheckIDHigh correlation
InvoiceAmt is highly overall correlated with DistributionAmt and 1 other fieldsHigh correlation
DistributionAmt is highly overall correlated with InvoiceAmtHigh correlation
CheckID is highly overall correlated with InvoiceIDHigh correlation
CheckAmt is highly overall correlated with InvoiceAmtHigh correlation
Budget_Type is highly overall correlated with ObjectId and 1 other fieldsHigh correlation
Agency_Name is highly overall correlated with ObjectId and 1 other fieldsHigh correlation
Budget_Type is highly imbalanced (71.5%)Imbalance
Category is highly imbalanced (59.0%)Imbalance
DepartmentName has 5230 (5.4%) missing valuesMissing
Sub_DepartmentName has 56189 (57.7%) missing valuesMissing
Stimulus_Type has 97463 (100.0%) missing valuesMissing
InvoiceAmt is highly skewed (γ1 = 29.3232093)Skewed
DistributionAmt is highly skewed (γ1 = 60.85704035)Skewed
ObjectId is uniformly distributedUniform
ObjectId has unique valuesUnique
Stimulus_Type is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-31 13:39:03.563754
Analysis finished2023-12-31 13:39:19.610514
Duration16.05 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

ObjectId
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct97463
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48732
Minimum1
Maximum97463
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size761.6 KiB
2023-12-31T19:09:19.784814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4874.1
Q124366.5
median48732
Q373097.5
95-th percentile92589.9
Maximum97463
Range97462
Interquartile range (IQR)48731

Descriptive statistics

Standard deviation28135.289
Coefficient of variation (CV)0.57734731
Kurtosis-1.2
Mean48732
Median Absolute Deviation (MAD)24366
Skewness0
Sum4.7495669 × 109
Variance7.9159449 × 108
MonotonicityStrictly increasing
2023-12-31T19:09:20.156758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
64973 1
 
< 0.1%
64982 1
 
< 0.1%
64981 1
 
< 0.1%
64980 1
 
< 0.1%
64979 1
 
< 0.1%
64978 1
 
< 0.1%
64977 1
 
< 0.1%
64976 1
 
< 0.1%
64975 1
 
< 0.1%
Other values (97453) 97453
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
97463 1
< 0.1%
97462 1
< 0.1%
97461 1
< 0.1%
97460 1
< 0.1%
97459 1
< 0.1%
97458 1
< 0.1%
97457 1
< 0.1%
97456 1
< 0.1%
97455 1
< 0.1%
97454 1
< 0.1%

Fiscal_Year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size761.6 KiB
2016
97463 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters389852
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 97463
100.0%

Length

2023-12-31T19:09:20.456100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T19:09:20.659257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 97463
100.0%

Most occurring characters

ValueCountFrequency (%)
2 97463
25.0%
0 97463
25.0%
1 97463
25.0%
6 97463
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 389852
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 97463
25.0%
0 97463
25.0%
1 97463
25.0%
6 97463
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 389852
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 97463
25.0%
0 97463
25.0%
1 97463
25.0%
6 97463
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 389852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 97463
25.0%
0 97463
25.0%
1 97463
25.0%
6 97463
25.0%

Budget_Type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size761.6 KiB
Metro Government Operations
92626 
Metro Government Capital
 
4837

Length

Max length27
Median length27
Mean length26.851113
Min length24

Characters and Unicode

Total characters2616990
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMetro Government Capital
2nd rowMetro Government Capital
3rd rowMetro Government Capital
4th rowMetro Government Capital
5th rowMetro Government Capital

Common Values

ValueCountFrequency (%)
Metro Government Operations 92626
95.0%
Metro Government Capital 4837
 
5.0%

Length

2023-12-31T19:09:20.894307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T19:09:21.112878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
metro 97463
33.3%
government 97463
33.3%
operations 92626
31.7%
capital 4837
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e 385015
14.7%
t 292389
11.2%
n 287552
11.0%
r 287552
11.0%
o 287552
11.0%
194926
 
7.4%
a 102300
 
3.9%
p 97463
 
3.7%
i 97463
 
3.7%
M 97463
 
3.7%
Other values (7) 487315
18.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2129675
81.4%
Uppercase Letter 292389
 
11.2%
Space Separator 194926
 
7.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 385015
18.1%
t 292389
13.7%
n 287552
13.5%
r 287552
13.5%
o 287552
13.5%
a 102300
 
4.8%
p 97463
 
4.6%
i 97463
 
4.6%
m 97463
 
4.6%
v 97463
 
4.6%
Other values (2) 97463
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
M 97463
33.3%
G 97463
33.3%
O 92626
31.7%
C 4837
 
1.7%
Space Separator
ValueCountFrequency (%)
194926
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2422064
92.6%
Common 194926
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 385015
15.9%
t 292389
12.1%
n 287552
11.9%
r 287552
11.9%
o 287552
11.9%
a 102300
 
4.2%
p 97463
 
4.0%
i 97463
 
4.0%
M 97463
 
4.0%
m 97463
 
4.0%
Other values (6) 389852
16.1%
Common
ValueCountFrequency (%)
194926
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2616990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 385015
14.7%
t 292389
11.2%
n 287552
11.0%
r 287552
11.0%
o 287552
11.0%
194926
 
7.4%
a 102300
 
3.9%
p 97463
 
3.7%
i 97463
 
3.7%
M 97463
 
3.7%
Other values (7) 487315
18.6%

Agency_Name
Categorical

HIGH CORRELATION 

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size761.6 KiB
Community Services
15787 
Office of Management & Budget
12401 
Parks & Recreation
11596 
Public Works & Assets
6266 
Louisville Zoo
5925 
Other values (32)
45488 

Length

Max length46
Median length36
Mean length22.142608
Min length7

Characters and Unicode

Total characters2158085
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPublic Works & Assets Department
2nd rowPublic Works & Assets Department
3rd rowPublic Works & Assets Department
4th rowPublic Works & Assets Department
5th rowPublic Works & Assets Department

Common Values

ValueCountFrequency (%)
Community Services 15787
16.2%
Office of Management & Budget 12401
12.7%
Parks & Recreation 11596
11.9%
Public Works & Assets 6266
 
6.4%
Louisville Zoo 5925
 
6.1%
Louisville Free Public Library 5180
 
5.3%
Louisville Metro Police Department 5026
 
5.2%
Emergency Services 4742
 
4.9%
Other Elected Officials 3691
 
3.8%
Develop Louisville 3627
 
3.7%
Other values (27) 23222
23.8%

Length

2023-12-31T19:09:21.419041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
36837
 
12.1%
louisville 24956
 
8.2%
services 23976
 
7.8%
of 17003
 
5.6%
public 16503
 
5.4%
community 15787
 
5.2%
office 13038
 
4.3%
management 12437
 
4.1%
budget 12401
 
4.1%
parks 11596
 
3.8%
Other values (47) 120931
39.6%

Most occurring characters

ValueCountFrequency (%)
e 259971
 
12.0%
208002
 
9.6%
i 166453
 
7.7%
o 132687
 
6.1%
t 113463
 
5.3%
r 112674
 
5.2%
s 111340
 
5.2%
l 101531
 
4.7%
c 93272
 
4.3%
n 93156
 
4.3%
Other values (35) 765536
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1661267
77.0%
Uppercase Letter 251699
 
11.7%
Space Separator 208002
 
9.6%
Other Punctuation 37117
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 259971
15.6%
i 166453
10.0%
o 132687
 
8.0%
t 113463
 
6.8%
r 112674
 
6.8%
s 111340
 
6.7%
l 101531
 
6.1%
c 93272
 
5.6%
n 93156
 
5.6%
a 79109
 
4.8%
Other values (11) 397611
23.9%
Uppercase Letter
ValueCountFrequency (%)
P 33728
13.4%
L 30441
12.1%
S 24013
9.5%
C 22031
8.8%
M 21519
8.5%
O 20420
8.1%
D 17384
 
6.9%
R 13837
 
5.5%
B 12401
 
4.9%
W 11323
 
4.5%
Other values (10) 44602
17.7%
Other Punctuation
ValueCountFrequency (%)
& 36645
98.7%
' 244
 
0.7%
/ 228
 
0.6%
Space Separator
ValueCountFrequency (%)
208002
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1912966
88.6%
Common 245119
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 259971
13.6%
i 166453
 
8.7%
o 132687
 
6.9%
t 113463
 
5.9%
r 112674
 
5.9%
s 111340
 
5.8%
l 101531
 
5.3%
c 93272
 
4.9%
n 93156
 
4.9%
a 79109
 
4.1%
Other values (31) 649310
33.9%
Common
ValueCountFrequency (%)
208002
84.9%
& 36645
 
14.9%
' 244
 
0.1%
/ 228
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2158085
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 259971
 
12.0%
208002
 
9.6%
i 166453
 
7.7%
o 132687
 
6.1%
t 113463
 
5.3%
r 112674
 
5.2%
s 111340
 
5.2%
l 101531
 
4.7%
c 93272
 
4.3%
n 93156
 
4.3%
Other values (35) 765536
35.5%
Distinct515
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size761.6 KiB
2023-12-31T19:09:21.879897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length55
Mean length20.604383
Min length6

Characters and Unicode

Total characters2008165
Distinct characters73
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique119 ?
Unique (%)0.1%

Sample

1st rowBridge Repair and Replacement
2nd rowBridge Repair and Replacement
3rd rowBridge Repair and Replacement
4th rowBridge Repair and Replacement
5th rowBridge Repair and Replacement
ValueCountFrequency (%)
25770
 
10.0%
services 23567
 
9.1%
community 17189
 
6.7%
facilities 12856
 
5.0%
fleet 11338
 
4.4%
support 10004
 
3.9%
operations 6899
 
2.7%
maintenance 6099
 
2.4%
public 5608
 
2.2%
administrative 5107
 
2.0%
Other values (793) 133306
51.7%
2023-12-31T19:09:22.701698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 200782
 
10.0%
i 197159
 
9.8%
160280
 
8.0%
t 147326
 
7.3%
n 124696
 
6.2%
r 120317
 
6.0%
o 107652
 
5.4%
s 100349
 
5.0%
a 97947
 
4.9%
c 74402
 
3.7%
Other values (63) 677255
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1560306
77.7%
Uppercase Letter 246232
 
12.3%
Space Separator 160280
 
8.0%
Other Punctuation 29971
 
1.5%
Decimal Number 9279
 
0.5%
Close Punctuation 917
 
< 0.1%
Open Punctuation 917
 
< 0.1%
Dash Punctuation 263
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 200782
12.9%
i 197159
12.6%
t 147326
9.4%
n 124696
 
8.0%
r 120317
 
7.7%
o 107652
 
6.9%
s 100349
 
6.4%
a 97947
 
6.3%
c 74402
 
4.8%
l 69498
 
4.5%
Other values (16) 320178
20.5%
Uppercase Letter
ValueCountFrequency (%)
S 45994
18.7%
C 31163
12.7%
F 30995
12.6%
P 20939
8.5%
A 18133
 
7.4%
O 15678
 
6.4%
D 12415
 
5.0%
M 11268
 
4.6%
R 10031
 
4.1%
B 7776
 
3.2%
Other values (16) 41840
17.0%
Decimal Number
ValueCountFrequency (%)
1 4540
48.9%
6 1737
 
18.7%
5 865
 
9.3%
9 816
 
8.8%
0 478
 
5.2%
2 366
 
3.9%
4 321
 
3.5%
8 70
 
0.8%
3 64
 
0.7%
7 22
 
0.2%
Other Punctuation
ValueCountFrequency (%)
& 25700
85.7%
/ 1865
 
6.2%
' 1626
 
5.4%
, 604
 
2.0%
. 167
 
0.6%
# 8
 
< 0.1%
: 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
160280
100.0%
Close Punctuation
ValueCountFrequency (%)
) 917
100.0%
Open Punctuation
ValueCountFrequency (%)
( 917
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 263
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1806538
90.0%
Common 201627
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 200782
 
11.1%
i 197159
 
10.9%
t 147326
 
8.2%
n 124696
 
6.9%
r 120317
 
6.7%
o 107652
 
6.0%
s 100349
 
5.6%
a 97947
 
5.4%
c 74402
 
4.1%
l 69498
 
3.8%
Other values (42) 566410
31.4%
Common
ValueCountFrequency (%)
160280
79.5%
& 25700
 
12.7%
1 4540
 
2.3%
/ 1865
 
0.9%
6 1737
 
0.9%
' 1626
 
0.8%
) 917
 
0.5%
( 917
 
0.5%
5 865
 
0.4%
9 816
 
0.4%
Other values (11) 2364
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2008165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 200782
 
10.0%
i 197159
 
9.8%
160280
 
8.0%
t 147326
 
7.3%
n 124696
 
6.2%
r 120317
 
6.0%
o 107652
 
5.4%
s 100349
 
5.0%
a 97947
 
4.9%
c 74402
 
3.7%
Other values (63) 677255
33.7%

DepartmentName
Text

MISSING 

Distinct252
Distinct (%)0.3%
Missing5230
Missing (%)5.4%
Memory size761.6 KiB
2023-12-31T19:09:23.118064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length33
Mean length17.809558
Min length3

Characters and Unicode

Total characters1642629
Distinct characters67
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st rowFinance & Administration
2nd rowFinance & Administration
3rd rowFinance & Administration
4th rowFinance & Administration
5th rowFinance & Administration
ValueCountFrequency (%)
services 15357
 
7.5%
14634
 
7.2%
management 10262
 
5.0%
facilities 9488
 
4.6%
self 6151
 
3.0%
sufficiency 6151
 
3.0%
maintenance 5259
 
2.6%
operations 4493
 
2.2%
policy 3830
 
1.9%
advocacy 3792
 
1.9%
Other values (349) 124962
61.1%
2023-12-31T19:09:23.917523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 188832
 
11.5%
i 159513
 
9.7%
n 118293
 
7.2%
112146
 
6.8%
a 110145
 
6.7%
t 96229
 
5.9%
c 87291
 
5.3%
r 84182
 
5.1%
s 71562
 
4.4%
o 67989
 
4.1%
Other values (57) 546447
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1300709
79.2%
Uppercase Letter 207512
 
12.6%
Space Separator 112146
 
6.8%
Other Punctuation 20010
 
1.2%
Decimal Number 2246
 
0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 188832
14.5%
i 159513
12.3%
n 118293
9.1%
a 110145
8.5%
t 96229
 
7.4%
c 87291
 
6.7%
r 84182
 
6.5%
s 71562
 
5.5%
o 67989
 
5.2%
l 67963
 
5.2%
Other values (16) 248710
19.1%
Uppercase Letter
ValueCountFrequency (%)
S 42527
20.5%
M 23990
11.6%
A 19565
9.4%
F 18942
9.1%
P 15706
 
7.6%
C 10534
 
5.1%
O 9127
 
4.4%
D 8755
 
4.2%
R 8271
 
4.0%
G 7188
 
3.5%
Other values (13) 42907
20.7%
Decimal Number
ValueCountFrequency (%)
1 702
31.3%
2 451
20.1%
5 215
 
9.6%
4 186
 
8.3%
3 182
 
8.1%
9 153
 
6.8%
7 110
 
4.9%
6 107
 
4.8%
0 73
 
3.3%
8 67
 
3.0%
Other Punctuation
ValueCountFrequency (%)
& 14633
73.1%
/ 2407
 
12.0%
' 2060
 
10.3%
, 907
 
4.5%
? 3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
112146
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1508221
91.8%
Common 134408
 
8.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 188832
12.5%
i 159513
 
10.6%
n 118293
 
7.8%
a 110145
 
7.3%
t 96229
 
6.4%
c 87291
 
5.8%
r 84182
 
5.6%
s 71562
 
4.7%
o 67989
 
4.5%
l 67963
 
4.5%
Other values (39) 456222
30.2%
Common
ValueCountFrequency (%)
112146
83.4%
& 14633
 
10.9%
/ 2407
 
1.8%
' 2060
 
1.5%
, 907
 
0.7%
1 702
 
0.5%
2 451
 
0.3%
5 215
 
0.2%
4 186
 
0.1%
3 182
 
0.1%
Other values (8) 519
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1642629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 188832
 
11.5%
i 159513
 
9.7%
n 118293
 
7.2%
112146
 
6.8%
a 110145
 
6.7%
t 96229
 
5.9%
c 87291
 
5.3%
r 84182
 
5.1%
s 71562
 
4.4%
o 67989
 
4.1%
Other values (57) 546447
33.3%

Sub_DepartmentName
Text

MISSING 

Distinct89
Distinct (%)0.2%
Missing56189
Missing (%)57.7%
Memory size761.6 KiB
2023-12-31T19:09:24.328426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length38
Mean length23.512599
Min length8

Characters and Unicode

Total characters970459
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowFinance/Administrative Support
2nd rowFinance/Administrative Support
3rd rowFinance/Administrative Support
4th rowFinance/Administrative Support
5th rowFinance/Administrative Support
ValueCountFrequency (%)
management 8228
 
6.9%
facilities 8174
 
6.9%
services 7639
 
6.4%
6542
 
5.5%
administration 5544
 
4.7%
care 4968
 
4.2%
of 4808
 
4.0%
grant 4769
 
4.0%
continuum 4744
 
4.0%
support 4451
 
3.7%
Other values (136) 59031
49.6%
2023-12-31T19:09:25.105901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 96884
 
10.0%
i 95670
 
9.9%
n 86352
 
8.9%
77624
 
8.0%
t 75992
 
7.8%
a 65111
 
6.7%
r 56286
 
5.8%
s 46957
 
4.8%
o 42660
 
4.4%
m 35129
 
3.6%
Other values (48) 291794
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 761842
78.5%
Uppercase Letter 120769
 
12.4%
Space Separator 77624
 
8.0%
Other Punctuation 9009
 
0.9%
Dash Punctuation 1183
 
0.1%
Open Punctuation 16
 
< 0.1%
Close Punctuation 16
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 96884
12.7%
i 95670
12.6%
n 86352
11.3%
t 75992
10.0%
a 65111
8.5%
r 56286
7.4%
s 46957
 
6.2%
o 42660
 
5.6%
m 35129
 
4.6%
l 31422
 
4.1%
Other values (16) 129379
17.0%
Uppercase Letter
ValueCountFrequency (%)
S 21503
17.8%
F 19090
15.8%
C 16973
14.1%
M 13399
11.1%
A 10442
8.6%
G 8170
 
6.8%
P 8081
 
6.7%
E 3728
 
3.1%
I 3529
 
2.9%
V 3178
 
2.6%
Other values (13) 12676
10.5%
Other Punctuation
ValueCountFrequency (%)
& 6318
70.1%
/ 2460
 
27.3%
? 224
 
2.5%
. 6
 
0.1%
' 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
77624
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1183
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 882611
90.9%
Common 87848
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 96884
 
11.0%
i 95670
 
10.8%
n 86352
 
9.8%
t 75992
 
8.6%
a 65111
 
7.4%
r 56286
 
6.4%
s 46957
 
5.3%
o 42660
 
4.8%
m 35129
 
4.0%
l 31422
 
3.6%
Other values (39) 250148
28.3%
Common
ValueCountFrequency (%)
77624
88.4%
& 6318
 
7.2%
/ 2460
 
2.8%
- 1183
 
1.3%
? 224
 
0.3%
( 16
 
< 0.1%
) 16
 
< 0.1%
. 6
 
< 0.1%
' 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 970459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 96884
 
10.0%
i 95670
 
9.9%
n 86352
 
8.9%
77624
 
8.0%
t 75992
 
7.8%
a 65111
 
6.7%
r 56286
 
5.8%
s 46957
 
4.8%
o 42660
 
4.4%
m 35129
 
3.6%
Other values (48) 291794
30.1%

Category
Categorical

IMBALANCE 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size761.6 KiB
Contractual Services
59109 
Supplies
33986 
Direct Reimbursements
 
2443
Equipment/Capital Outlay
 
1557
Other Expenses
 
308
Other values (3)
 
60

Length

Max length24
Median length20
Mean length15.886306
Min length8

Characters and Unicode

Total characters1548327
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowContractual Services
2nd rowContractual Services
3rd rowContractual Services
4th rowContractual Services
5th rowContractual Services

Common Values

ValueCountFrequency (%)
Contractual Services 59109
60.6%
Supplies 33986
34.9%
Direct Reimbursements 2443
 
2.5%
Equipment/Capital Outlay 1557
 
1.6%
Other Expenses 308
 
0.3%
Interdepartment Charges 39
 
< 0.1%
Personnel Services 20
 
< 0.1%
Interagency Charges 1
 
< 0.1%

Length

2023-12-31T19:09:25.437848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T19:09:25.706895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
services 59129
36.7%
contractual 59109
36.7%
supplies 33986
21.1%
direct 2443
 
1.5%
reimbursements 2443
 
1.5%
equipment/capital 1557
 
1.0%
outlay 1557
 
1.0%
other 308
 
0.2%
expenses 308
 
0.2%
charges 40
 
< 0.1%
Other values (3) 60
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 164696
10.6%
t 128201
 
8.3%
r 123571
 
8.0%
a 122969
 
7.9%
c 120682
 
7.8%
i 101115
 
6.5%
s 98677
 
6.4%
u 98652
 
6.4%
l 96229
 
6.2%
S 93115
 
6.0%
Other values (21) 400420
25.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1320796
85.3%
Uppercase Letter 162497
 
10.5%
Space Separator 63477
 
4.1%
Other Punctuation 1557
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 164696
12.5%
t 128201
9.7%
r 123571
9.4%
a 122969
9.3%
c 120682
9.1%
i 101115
7.7%
s 98677
7.5%
u 98652
7.5%
l 96229
7.3%
p 71433
 
5.4%
Other values (11) 194571
14.7%
Uppercase Letter
ValueCountFrequency (%)
S 93115
57.3%
C 60706
37.4%
D 2443
 
1.5%
R 2443
 
1.5%
O 1865
 
1.1%
E 1865
 
1.1%
I 40
 
< 0.1%
P 20
 
< 0.1%
Space Separator
ValueCountFrequency (%)
63477
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1557
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1483293
95.8%
Common 65034
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 164696
11.1%
t 128201
 
8.6%
r 123571
 
8.3%
a 122969
 
8.3%
c 120682
 
8.1%
i 101115
 
6.8%
s 98677
 
6.7%
u 98652
 
6.7%
l 96229
 
6.5%
S 93115
 
6.3%
Other values (19) 335386
22.6%
Common
ValueCountFrequency (%)
63477
97.6%
/ 1557
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1548327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 164696
10.6%
t 128201
 
8.3%
r 123571
 
8.0%
a 122969
 
7.9%
c 120682
 
7.8%
i 101115
 
6.5%
s 98677
 
6.4%
u 98652
 
6.4%
l 96229
 
6.2%
S 93115
 
6.0%
Other values (21) 400420
25.9%
Distinct398
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size761.6 KiB
2023-12-31T19:09:26.090549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length38
Mean length23.131291
Min length4

Characters and Unicode

Total characters2254445
Distinct characters65
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)< 0.1%

Sample

1st rowBridge Construction/ Repair Services
2nd rowBridge Construction/ Repair Services
3rd rowBridge Construction/ Repair Services
4th rowBridge Construction/ Repair Services
5th rowBridge Construction/ Repair Services
ValueCountFrequency (%)
services 31663
 
12.2%
supplies 25805
 
10.0%
grant 12303
 
4.7%
assistance 7334
 
2.8%
community 6966
 
2.7%
equipment 6639
 
2.6%
office 6108
 
2.4%
professional 5907
 
2.3%
maintenance 5821
 
2.2%
5434
 
2.1%
Other values (470) 145353
56.0%
2023-12-31T19:09:26.825904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 241441
 
10.7%
i 196788
 
8.7%
161870
 
7.2%
n 157420
 
7.0%
s 144319
 
6.4%
r 134444
 
6.0%
t 128618
 
5.7%
a 126423
 
5.6%
c 97837
 
4.3%
o 95736
 
4.2%
Other values (55) 769549
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1804642
80.0%
Uppercase Letter 263429
 
11.7%
Space Separator 161870
 
7.2%
Other Punctuation 22430
 
1.0%
Decimal Number 763
 
< 0.1%
Dash Punctuation 663
 
< 0.1%
Open Punctuation 324
 
< 0.1%
Close Punctuation 324
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 241441
13.4%
i 196788
10.9%
n 157420
8.7%
s 144319
 
8.0%
r 134444
 
7.4%
t 128618
 
7.1%
a 126423
 
7.0%
c 97837
 
5.4%
o 95736
 
5.3%
l 91969
 
5.1%
Other values (16) 389647
21.6%
Uppercase Letter
ValueCountFrequency (%)
S 67872
25.8%
C 28165
10.7%
A 21830
 
8.3%
E 19439
 
7.4%
P 16605
 
6.3%
R 15627
 
5.9%
M 15489
 
5.9%
G 13959
 
5.3%
O 12121
 
4.6%
F 10758
 
4.1%
Other values (12) 41564
15.8%
Decimal Number
ValueCountFrequency (%)
0 243
31.8%
1 170
22.3%
2 106
13.9%
5 53
 
6.9%
3 43
 
5.6%
4 37
 
4.8%
6 33
 
4.3%
9 30
 
3.9%
8 28
 
3.7%
7 20
 
2.6%
Other Punctuation
ValueCountFrequency (%)
/ 16477
73.5%
& 4943
 
22.0%
, 1010
 
4.5%
Space Separator
ValueCountFrequency (%)
161870
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 663
100.0%
Open Punctuation
ValueCountFrequency (%)
( 324
100.0%
Close Punctuation
ValueCountFrequency (%)
) 324
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2068071
91.7%
Common 186374
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 241441
 
11.7%
i 196788
 
9.5%
n 157420
 
7.6%
s 144319
 
7.0%
r 134444
 
6.5%
t 128618
 
6.2%
a 126423
 
6.1%
c 97837
 
4.7%
o 95736
 
4.6%
l 91969
 
4.4%
Other values (38) 653076
31.6%
Common
ValueCountFrequency (%)
161870
86.9%
/ 16477
 
8.8%
& 4943
 
2.7%
, 1010
 
0.5%
- 663
 
0.4%
( 324
 
0.2%
) 324
 
0.2%
0 243
 
0.1%
1 170
 
0.1%
2 106
 
0.1%
Other values (7) 244
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2254445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 241441
 
10.7%
i 196788
 
8.7%
161870
 
7.2%
n 157420
 
7.0%
s 144319
 
6.4%
r 134444
 
6.0%
t 128618
 
5.7%
a 126423
 
5.6%
c 97837
 
4.3%
o 95736
 
4.2%
Other values (55) 769549
34.1%

Stimulus_Type
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing97463
Missing (%)100.0%
Memory size761.6 KiB
Distinct68
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size761.6 KiB
2023-12-31T19:09:27.248477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length12
Mean length12.806973
Min length3

Characters and Unicode

Total characters1248206
Distinct characters64
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowCounty Road Aid
2nd rowCounty Road Aid
3rd rowCounty Road Aid
4th rowCounty Road Aid
5th rowCounty Road Aid
ValueCountFrequency (%)
fund 76489
35.9%
general 73941
34.7%
shelter 4968
 
2.3%
plus 4968
 
2.3%
care 4968
 
2.3%
caa 2833
 
1.3%
2015 2130
 
1.0%
federally 1993
 
0.9%
funded 1993
 
0.9%
go 1899
 
0.9%
Other values (125) 37157
17.4%
2023-12-31T19:09:27.985181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 183666
14.7%
n 161873
13.0%
115876
9.3%
r 94935
7.6%
l 93064
7.5%
a 92335
7.4%
u 88561
7.1%
d 88365
7.1%
F 84320
6.8%
G 78022
6.3%
Other values (54) 167189
13.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 878145
70.4%
Uppercase Letter 235231
 
18.8%
Space Separator 115876
 
9.3%
Decimal Number 17095
 
1.4%
Dash Punctuation 1702
 
0.1%
Other Punctuation 133
 
< 0.1%
Open Punctuation 12
 
< 0.1%
Close Punctuation 12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 183666
20.9%
n 161873
18.4%
r 94935
10.8%
l 93064
10.6%
a 92335
10.5%
u 88561
10.1%
d 88365
10.1%
t 17887
 
2.0%
i 10883
 
1.2%
s 10358
 
1.2%
Other values (15) 36218
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
F 84320
35.8%
G 78022
33.2%
C 14572
 
6.2%
A 11834
 
5.0%
P 8542
 
3.6%
S 8267
 
3.5%
M 5444
 
2.3%
O 3956
 
1.7%
E 3748
 
1.6%
B 3352
 
1.4%
Other values (13) 13174
 
5.6%
Decimal Number
ValueCountFrequency (%)
1 4498
26.3%
0 4161
24.3%
2 3990
23.3%
5 3482
20.4%
4 592
 
3.5%
9 273
 
1.6%
3 89
 
0.5%
6 6
 
< 0.1%
7 4
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 106
79.7%
' 25
 
18.8%
. 2
 
1.5%
Space Separator
ValueCountFrequency (%)
115876
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1702
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1113376
89.2%
Common 134830
 
10.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 183666
16.5%
n 161873
14.5%
r 94935
8.5%
l 93064
8.4%
a 92335
8.3%
u 88561
8.0%
d 88365
7.9%
F 84320
7.6%
G 78022
7.0%
t 17887
 
1.6%
Other values (38) 130348
11.7%
Common
ValueCountFrequency (%)
115876
85.9%
1 4498
 
3.3%
0 4161
 
3.1%
2 3990
 
3.0%
5 3482
 
2.6%
- 1702
 
1.3%
4 592
 
0.4%
9 273
 
0.2%
/ 106
 
0.1%
3 89
 
0.1%
Other values (6) 61
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1248206
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 183666
14.7%
n 161873
13.0%
115876
9.3%
r 94935
7.6%
l 93064
7.5%
a 92335
7.4%
u 88561
7.1%
d 88365
7.1%
F 84320
6.8%
G 78022
6.3%
Other values (54) 167189
13.4%
Distinct5177
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size761.6 KiB
2023-12-31T19:09:28.449075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length93
Median length62
Mean length21.84787
Min length2

Characters and Unicode

Total characters2129359
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1884 ?
Unique (%)1.9%

Sample

1st rowHIGHWAY SAFETY SERVICES
2nd rowHIGHWAY SAFETY SERVICES
3rd rowHIGHWAY SAFETY SERVICES
4th rowHIGHWAY SAFETY SERVICES
5th rowHIGHWAY SAFETY SERVICES
ValueCountFrequency (%)
inc 38584
 
11.4%
llc 10630
 
3.2%
7562
 
2.2%
company 6730
 
2.0%
co 6257
 
1.9%
of 5402
 
1.6%
depot 4974
 
1.5%
office 4446
 
1.3%
supply 4202
 
1.2%
services 3106
 
0.9%
Other values (5832) 245296
72.7%
2023-12-31T19:09:29.251782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
239726
11.3%
E 186192
 
8.7%
I 164902
 
7.7%
N 161952
 
7.6%
R 136633
 
6.4%
C 135675
 
6.4%
A 135622
 
6.4%
O 128276
 
6.0%
S 124801
 
5.9%
L 119874
 
5.6%
Other values (35) 595706
28.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1873627
88.0%
Space Separator 239726
 
11.3%
Other Punctuation 12440
 
0.6%
Decimal Number 1734
 
0.1%
Dash Punctuation 1196
 
0.1%
Open Punctuation 318
 
< 0.1%
Close Punctuation 318
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 186192
 
9.9%
I 164902
 
8.8%
N 161952
 
8.6%
R 136633
 
7.3%
C 135675
 
7.2%
A 135622
 
7.2%
O 128276
 
6.8%
S 124801
 
6.7%
L 119874
 
6.4%
T 106373
 
5.7%
Other values (16) 473327
25.3%
Decimal Number
ValueCountFrequency (%)
1 474
27.3%
9 267
15.4%
0 257
14.8%
8 217
12.5%
2 187
 
10.8%
4 157
 
9.1%
7 67
 
3.9%
3 46
 
2.7%
5 41
 
2.4%
6 21
 
1.2%
Other Punctuation
ValueCountFrequency (%)
& 11755
94.5%
. 421
 
3.4%
/ 155
 
1.2%
' 100
 
0.8%
# 9
 
0.1%
Space Separator
ValueCountFrequency (%)
239726
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1196
100.0%
Open Punctuation
ValueCountFrequency (%)
( 318
100.0%
Close Punctuation
ValueCountFrequency (%)
) 318
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1873627
88.0%
Common 255732
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 186192
 
9.9%
I 164902
 
8.8%
N 161952
 
8.6%
R 136633
 
7.3%
C 135675
 
7.2%
A 135622
 
7.2%
O 128276
 
6.8%
S 124801
 
6.7%
L 119874
 
6.4%
T 106373
 
5.7%
Other values (16) 473327
25.3%
Common
ValueCountFrequency (%)
239726
93.7%
& 11755
 
4.6%
- 1196
 
0.5%
1 474
 
0.2%
. 421
 
0.2%
( 318
 
0.1%
) 318
 
0.1%
9 267
 
0.1%
0 257
 
0.1%
8 217
 
0.1%
Other values (9) 783
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2129359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
239726
11.3%
E 186192
 
8.7%
I 164902
 
7.7%
N 161952
 
7.6%
R 136633
 
6.4%
C 135675
 
6.4%
A 135622
 
6.4%
O 128276
 
6.0%
S 124801
 
5.9%
L 119874
 
5.6%
Other values (35) 595706
28.0%

InvoiceID
Real number (ℝ)

HIGH CORRELATION 

Distinct78363
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1669279.6
Minimum1534328
Maximum1724826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size761.6 KiB
2023-12-31T19:09:29.559642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1534328
5-th percentile1625181
Q11644688.5
median1669511
Q31693566.5
95-th percentile1713899.9
Maximum1724826
Range190498
Interquartile range (IQR)48878

Descriptive statistics

Standard deviation28556.811
Coefficient of variation (CV)0.017107267
Kurtosis-1.13785
Mean1669279.6
Median Absolute Deviation (MAD)24516
Skewness-0.003472627
Sum1.6269299 × 1011
Variance8.1549145 × 108
MonotonicityNot monotonic
2023-12-31T19:09:29.879946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1647870 60
 
0.1%
1715811 42
 
< 0.1%
1651511 37
 
< 0.1%
1682576 37
 
< 0.1%
1674395 36
 
< 0.1%
1709592 35
 
< 0.1%
1655611 35
 
< 0.1%
1704776 35
 
< 0.1%
1695885 35
 
< 0.1%
1711564 35
 
< 0.1%
Other values (78353) 97076
99.6%
ValueCountFrequency (%)
1534328 3
< 0.1%
1562460 3
< 0.1%
1574774 3
< 0.1%
1576636 1
 
< 0.1%
1578381 3
< 0.1%
1582040 3
< 0.1%
1588962 1
 
< 0.1%
1589092 1
 
< 0.1%
1592147 2
< 0.1%
1593516 1
 
< 0.1%
ValueCountFrequency (%)
1724826 3
 
< 0.1%
1724357 1
 
< 0.1%
1724262 1
 
< 0.1%
1722877 1
 
< 0.1%
1722871 3
 
< 0.1%
1722862 1
 
< 0.1%
1722859 20
< 0.1%
1722821 1
 
< 0.1%
1722806 1
 
< 0.1%
1722799 1
 
< 0.1%
Distinct673
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size761.6 KiB
Minimum2009-12-01 05:00:00+00:00
Maximum2016-07-31 03:59:59+00:00
2023-12-31T19:09:30.197654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:30.499269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

InvoiceAmt
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct33924
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5259.9736
Minimum-111520.18
Maximum3297990
Zeros0
Zeros (%)0.0%
Negative812
Negative (%)0.8%
Memory size761.6 KiB
2023-12-31T19:09:30.790109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-111520.18
5-th percentile15.5
Q190.91
median315
Q31046.05
95-th percentile14242.21
Maximum3297990
Range3409510.2
Interquartile range (IQR)955.14

Descriptive statistics

Standard deviation37983.479
Coefficient of variation (CV)7.2212301
Kurtosis1735.9058
Mean5259.9736
Median Absolute Deviation (MAD)270.37
Skewness29.323209
Sum5.126528 × 108
Variance1.4427447 × 109
MonotonicityNot monotonic
2023-12-31T19:09:31.089042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 1688
 
1.7%
300 842
 
0.9%
50 637
 
0.7%
100 585
 
0.6%
315 574
 
0.6%
30 562
 
0.6%
125 557
 
0.6%
500 353
 
0.4%
600 351
 
0.4%
200 318
 
0.3%
Other values (33914) 90996
93.4%
ValueCountFrequency (%)
-111520.18 1
< 0.1%
-9465.41 1
< 0.1%
-8477.91 1
< 0.1%
-8068.5 2
< 0.1%
-7580 1
< 0.1%
-7319.69 1
< 0.1%
-7040.16 1
< 0.1%
-5831.44 1
< 0.1%
-5672.35 1
< 0.1%
-5172.68 1
< 0.1%
ValueCountFrequency (%)
3297990 1
< 0.1%
3102226.8 1
< 0.1%
2968700 1
< 0.1%
1938891.75 1
< 0.1%
1896241.22 1
< 0.1%
1721522.1 1
< 0.1%
1441361 1
< 0.1%
1369957 1
< 0.1%
1331341 1
< 0.1%
998639 1
< 0.1%

DistributionAmt
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct39591
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2585.5885
Minimum-430882.42
Maximum3297990
Zeros17
Zeros (%)< 0.1%
Negative2838
Negative (%)2.9%
Memory size761.6 KiB
2023-12-31T19:09:31.379804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-430882.42
5-th percentile4.58
Q149.13
median210
Q3661.48
95-th percentile7740
Maximum3297990
Range3728872.4
Interquartile range (IQR)612.35

Descriptive statistics

Standard deviation28069.042
Coefficient of variation (CV)10.855959
Kurtosis5640.5785
Mean2585.5885
Median Absolute Deviation (MAD)190
Skewness60.85704
Sum2.5199921 × 108
Variance7.8787111 × 108
MonotonicityNot monotonic
2023-12-31T19:09:31.686194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 1678
 
1.7%
300 844
 
0.9%
50 655
 
0.7%
100 585
 
0.6%
315 560
 
0.6%
30 535
 
0.5%
125 511
 
0.5%
500 384
 
0.4%
600 352
 
0.4%
200 338
 
0.3%
Other values (39581) 91021
93.4%
ValueCountFrequency (%)
-430882.42 1
< 0.1%
-317744.15 1
< 0.1%
-300000 1
< 0.1%
-267716.28 1
< 0.1%
-225000 1
< 0.1%
-132978.84 1
< 0.1%
-125000 1
< 0.1%
-111520.18 1
< 0.1%
-100643.04 1
< 0.1%
-100463.04 1
< 0.1%
ValueCountFrequency (%)
3297990 1
< 0.1%
3102226.8 1
< 0.1%
2968700 1
< 0.1%
1938891.75 1
< 0.1%
1896241.22 1
< 0.1%
1721522.1 1
< 0.1%
1441361 1
< 0.1%
1369957 1
< 0.1%
1331341 1
< 0.1%
998639 1
< 0.1%

CheckID
Real number (ℝ)

HIGH CORRELATION 

Distinct45211
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean830373.01
Minimum794385
Maximum873879
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size761.6 KiB
2023-12-31T19:09:31.996414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum794385
5-th percentile801133.1
Q1814039.5
median830657
Q3846600
95-th percentile859409
Maximum873879
Range79494
Interquartile range (IQR)32560.5

Descriptive statistics

Standard deviation18790.533
Coefficient of variation (CV)0.022629026
Kurtosis-1.1601541
Mean830373.01
Median Absolute Deviation (MAD)16134
Skewness-0.0049543999
Sum8.0930645 × 1010
Variance3.5308412 × 108
MonotonicityNot monotonic
2023-12-31T19:09:32.307936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
832858 96
 
0.1%
803645 91
 
0.1%
843111 87
 
0.1%
840696 85
 
0.1%
836201 85
 
0.1%
809212 84
 
0.1%
858735 80
 
0.1%
829908 74
 
0.1%
843783 74
 
0.1%
815397 68
 
0.1%
Other values (45201) 96639
99.2%
ValueCountFrequency (%)
794385 1
< 0.1%
794426 1
< 0.1%
794541 1
< 0.1%
794719 1
< 0.1%
794735 1
< 0.1%
794748 1
< 0.1%
794756 1
< 0.1%
794758 1
< 0.1%
794775 1
< 0.1%
794877 1
< 0.1%
ValueCountFrequency (%)
873879 1
< 0.1%
873873 2
< 0.1%
873830 1
< 0.1%
873820 1
< 0.1%
873387 2
< 0.1%
873380 1
< 0.1%
873362 1
< 0.1%
873217 1
< 0.1%
873205 1
< 0.1%
873032 2
< 0.1%
Distinct309
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size761.6 KiB
Minimum2015-07-02 03:59:59+00:00
Maximum2016-09-23 03:59:59+00:00
2023-12-31T19:09:32.621513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:32.941129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CheckAmt
Real number (ℝ)

HIGH CORRELATION 

Distinct22752
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10624.83
Minimum0
Maximum3297990
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size761.6 KiB
2023-12-31T19:09:33.282536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51.3
Q1309.21
median976.5
Q34313.45
95-th percentile33578.25
Maximum3297990
Range3297990
Interquartile range (IQR)4004.24

Descriptive statistics

Standard deviation51854.389
Coefficient of variation (CV)4.8804913
Kurtosis565.69533
Mean10624.83
Median Absolute Deviation (MAD)858.9
Skewness16.499
Sum1.0355278 × 109
Variance2.6888777 × 109
MonotonicityNot monotonic
2023-12-31T19:09:33.606165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 1691
 
1.7%
300 799
 
0.8%
100 414
 
0.4%
50 376
 
0.4%
600 369
 
0.4%
500 309
 
0.3%
550 213
 
0.2%
150 211
 
0.2%
200 205
 
0.2%
250 201
 
0.2%
Other values (22742) 92675
95.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.1 1
 
< 0.1%
0.13 4
 
< 0.1%
0.59 2
 
< 0.1%
0.61 5
 
< 0.1%
0.93 1
 
< 0.1%
1 14
< 0.1%
1.01 1
 
< 0.1%
1.05 1
 
< 0.1%
1.18 2
 
< 0.1%
ValueCountFrequency (%)
3297990 1
< 0.1%
3102226.8 1
< 0.1%
2968700 1
< 0.1%
1938891.75 1
< 0.1%
1896241.22 1
< 0.1%
1721522.1 1
< 0.1%
1441361 1
< 0.1%
1433025 2
< 0.1%
1369957 1
< 0.1%
1098899.99 2
< 0.1%

CheckVoidDt
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size761.6 KiB
1900/01/01 05:00:00+00
97463 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2144186
Distinct characters8
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1900/01/01 05:00:00+00
2nd row1900/01/01 05:00:00+00
3rd row1900/01/01 05:00:00+00
4th row1900/01/01 05:00:00+00
5th row1900/01/01 05:00:00+00

Common Values

ValueCountFrequency (%)
1900/01/01 05:00:00+00 97463
100.0%

Length

2023-12-31T19:09:33.883634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-31T19:09:34.082250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1900/01/01 97463
50.0%
05:00:00+00 97463
50.0%

Most occurring characters

ValueCountFrequency (%)
0 1072093
50.0%
1 292389
 
13.6%
/ 194926
 
9.1%
: 194926
 
9.1%
9 97463
 
4.5%
97463
 
4.5%
5 97463
 
4.5%
+ 97463
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1559408
72.7%
Other Punctuation 389852
 
18.2%
Space Separator 97463
 
4.5%
Math Symbol 97463
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1072093
68.8%
1 292389
 
18.8%
9 97463
 
6.2%
5 97463
 
6.2%
Other Punctuation
ValueCountFrequency (%)
/ 194926
50.0%
: 194926
50.0%
Space Separator
ValueCountFrequency (%)
97463
100.0%
Math Symbol
ValueCountFrequency (%)
+ 97463
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2144186
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1072093
50.0%
1 292389
 
13.6%
/ 194926
 
9.1%
: 194926
 
9.1%
9 97463
 
4.5%
97463
 
4.5%
5 97463
 
4.5%
+ 97463
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2144186
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1072093
50.0%
1 292389
 
13.6%
/ 194926
 
9.1%
: 194926
 
9.1%
9 97463
 
4.5%
97463
 
4.5%
5 97463
 
4.5%
+ 97463
 
4.5%

Interactions

2023-12-31T19:09:16.316931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:08.909455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:10.239547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:11.654221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:13.462242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:14.896148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:16.527683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:09.120871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:10.452293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:11.883070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:13.687820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:15.113169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:16.804598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:09.354196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:10.689853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:12.124906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:13.946685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:15.381841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:17.016865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:09.576152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:10.922453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:12.369062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:14.181207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:15.609749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:17.231387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:09.800828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:11.166481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:12.663073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:14.423393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:15.849758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:17.455239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:10.008750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:11.412795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:13.243073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:14.661758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-31T19:09:16.079969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-31T19:09:34.220100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ObjectIdInvoiceIDInvoiceAmtDistributionAmtCheckIDCheckAmtBudget_TypeAgency_NameCategory
ObjectId1.000-0.024-0.067-0.088-0.0200.0470.6850.6800.223
InvoiceID-0.0241.0000.0080.0040.9920.0170.0420.0440.025
InvoiceAmt-0.0670.0081.0000.8160.0100.6150.0770.1230.024
DistributionAmt-0.0880.0040.8161.000-0.0010.4800.0630.0690.014
CheckID-0.0200.9920.010-0.0011.0000.0290.0450.0570.026
CheckAmt0.0470.0170.6150.4800.0291.0000.1400.1360.028
Budget_Type0.6850.0420.0770.0630.0450.1401.0000.7360.232
Agency_Name0.6800.0440.1230.0690.0570.1360.7361.0000.317
Category0.2230.0250.0240.0140.0260.0280.2320.3171.000

Missing values

2023-12-31T19:09:17.832577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-31T19:09:18.609979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-31T19:09:19.286115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ObjectIdFiscal_YearBudget_TypeAgency_NameSub_Agency_NameDepartmentNameSub_DepartmentNameCategorySub_CategoryStimulus_TypeFunding_SourceVendor_NameInvoiceIDInvoiceDtInvoiceAmtDistributionAmtCheckIDCheckDtCheckAmtCheckVoidDt
012016Metro Government CapitalPublic Works & Assets DepartmentBridge Repair and ReplacementNaNNaNContractual ServicesBridge Construction/ Repair ServicesNaNCounty Road AidHIGHWAY SAFETY SERVICES16433032015/09/30 03:59:59+005415.05415.08155292015/10/26 03:59:59+007580.001900/01/01 05:00:00+00
122016Metro Government CapitalPublic Works & Assets DepartmentBridge Repair and ReplacementNaNNaNContractual ServicesBridge Construction/ Repair ServicesNaNCounty Road AidHIGHWAY SAFETY SERVICES16270062015/07/31 03:59:59+005595.55595.58072882015/09/14 03:59:59+005595.501900/01/01 05:00:00+00
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